Linear Regression Analysis
A 3-Day Seminar Taught by Paul Allison, Ph.D.
Linear regression is the most widely-used method for the statistical analysis of non-experimental (observational) data. It’s also the essential foundation for understanding more advanced methods like logistic regression, survival analysis, multilevel modeling, and structural equation modeling. Without a thorough mastery of linear regression, there’s little point in trying to learn more complex regression methods.
If you’ve never had a course on linear regression, or if you took one so long ago that you have forgotten most of it, this seminar will get you up to speed. In three days, we’ll cover almost a semester’s worth of material. When it’s over, you’ll be a knowledgeable and effective user of regression methods. And you will have the necessary preparation to take most of Statistical Horizons’ more advanced seminars.
Paul Allison has been teaching courses on linear regression for more than 30 years. He is the author of the popular text, Multiple Regression, which provides a very practical, intuitive, and non-mathematical introduction to the topic of linear regression.
The seminar will begin by focusing on the two major goals of linear regression: prediction and hypothesis testing. We’ll look at several examples from published articles to see how linear regression is used in practice and how to interpret regression tables.
Next we’ll consider all the things that can go wrong when using linear regression, and we’ll see how to critique the analyses done by others.
We’ll delve into the mathematical theory behind linear regression, focusing on the essential assumptions, and on the implied properties of the least squares method. We’ll also spend considerable time on techniques for building non-linearity into linear regression by way of transformations, interactions, and dummy (indicator) variables.
There will be lots of hands-on exercises using either SAS or Stata.
This seminar will use both SAS and Stata for the many empirical examples and the exercises. At least one hour each day will be devoted to hands-on exercises. To optimally benefit, you should bring your own laptop with a recent version of SAS or Stata installed. Power outlets will be provided at each seat.
If you are unable to obtain access to the full versions of SAS or Stata, there is an option to obtain a trial version of Stata 13. Stata is licensed through StataCorp (www.stata.com) and is frequently offered at a significant discount through academic institutions to their employees and students. Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s 30-day software return policy and obtain Stata 13 on a trial basis in the weeks immediately preceding this course. Stata also has a 30-day trial-license “share” policy permitting current license-holders to share a trial copy: http://www.stata.com/customer-service/share-stata/
There is now a free version of SAS, called the SAS University Edition, that is available to anyone. It has everything needed to run the exercises in this course, and it will run on Windows, Mac or Linux computers. However, you do need a 64-bit machine with at least 1 GB of RAM. You also have to download and install virtualization software that is available free from third-party vendors. The SAS Studio interface runs in your browser, but you do not have to be connected to the Internet. The download and installation are a bit complicated, but well worth the time and effort.
WHO SHOULD ATTEND?
This seminar is designed for people who have a basic background in statistics, and who want to learn more about the theory and practice of linear regression. You’ll need to have taken an introductory course in statistics, and be comfortable with such concepts as random sampling, measures of center and variability, correlation, sampling distributions, standard errors, confidence intervals, and hypothesis testing. You should also have at least some experience using either SAS or Stata. Neither matrix algebra nor calculus will be used.
Although the course is relatively non-mathematical, considerable emphasis will be placed on the underlying assumptions and their implications. Upon completion of this seminar, you should be able to run your own linear regressions, build and evaluate regression models, and interpret and critique regression results.
LOCATION, Format AND MATERIALS
The course meets 9 a.m. to 5 p.m. on Thursday, June 4, Friday, June 5 and Saturday, June 6 at Temple University Center City, 1515 Market Street, Philadelphia, PA.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
REGISTRATION AND LODGING
The fee of $1395 includes all seminar materials.
Lodging Reservation Instructions
A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $147 per night for a Standard room. This location is about a 5 minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STA603. For guaranteed rate and availability, you must reserve your room no later than May 3, 2015.
- What is linear regression and what is it good for?
- Examples of published regression analyses and interpretation of results.
- The mechanics of regression in SAS and Stata.
- Bivariate and trivariate regression.
- Assumptions of linear regression and properties of least squares estimation.
- Evaluation of regression models.
- What can go wrong in linear regression.
- Regression, correlation, and standardized coefficients.
- Nonlinearity and interaction.
- Dummy (indicator) variables.
- Model building strategies.
- Missing data.
“Taking a course with the author of many major textbooks is invaluable. Three days with Paul Allison is like 3 months of a graduate level stats class. As a clinical researcher, I need to be able to do my own analyses and be able to communicate with the statistical consultants (PhDs usually) when I can’t do my analysis. This course will help me do both. The hands-on exercises, completed and reviewed during the class time, were incredibly helpful.”
Aileen Gariepy, Yale University
“I would strongly recommend this introductory regression course for anyone who wants to learn about regression. It helps one to understand the basic statistical principles, syntax in Stata or SAS, as well as interpretation of output.”
Van Doren Hsu, GDIT
“Dr. Allison is an excellent teacher, able to cover a wide range of material in a short time, while making it practicable, understandable, and still really interesting. I learned far more than I ever expected to in such a short time. I highly recommend this course to anyone looking to expand their analytical toolkit.”